On the Easy Use of Scientific Computing Services for Large Scale Linear Algebra and Parallel Decision Making with the P-Grade Portal

International audienceScientific research is becoming increasingly dependent on the large-scale analysis of data using distributed computing infrastructures (Grid, cloud, GPU, etc.). Scientific computing (Petitet et al. 1999) aims at constructing mathematical models and numerical solution techniques for solving problems arising in science and engineering. In this paper, we describe the services of an integrated portal based on the P-Grade (Parallel Grid Run-time and Application Development Environment) portal (http://www.p-grade.hu) that enables the solution of large-scale linear systems of equations using direct solvers, makes easier the use of parallel block iterative algorithm and provides an interface for parallel decision making algorithms. The ultimate goal is to develop a single sign on integrated multi-service environment providing an easy access to different kind of mathematical calculations and algorithms to be performed on hybrid distributed computing infrastructures combining the benefits of large clusters, Grid or cloud, when needed

Development of Web Environment for Efficient Exploitation of Linux Cluster Computing Resources 1

Abstract. Linux clusters are fast becoming a dominant architecture for highperformance computing. However, the management and efficient exploitation of these clusters across users, applications and data continues to be a timeconsuming and challenging task. The main goal of the article is the introduction of a Web-based environment for efficient exploitation of Linux clusters, which includes a MPI debugger library and resource reservation systems. The environment allows the simplicity and efficient use of the available computing resources.

Environmental Science Federated Cloud Platform in the BSEC Region

Abstract — Various types of research infrastructures available in the region of Black Sea Economic Cooperation (BSEC) make it possible to handle large data sets and provide significant computational resources. The environmental science community has a vital role in the region by exploiting the available computational and storage resources using several digital models, special data infrastructures and tools. The main aim of the article is not only to present the extended federated cloud platform for target user communities, but also to introduce the environmental science potential and geoprocessing facilities that may benefit from the suggested platform

Leading the way toward an environmental National Spatial Data Infrastructure in Armenia

Once the most industrialized republic of the Soviet Union, Armenia inherited a dramatic ecological situation from the Soviet era. As the key national environmental academic entity, the Center for Ecological-Noosphere Studies (CENS) of the National Academy of Sciences of the Republic of Armenia has a strong national role in delivering authoritative environmental information and data sets. To enhance data sharing towards its stakeholders, CENS engaged in recent years in several international capacity building projects directed to the setting up of an environmental Spatial Data Infrastructure (SDI). These activities were successful in showing the potential of data sharing in Armenia, to gain visibility in the country and the South Caucasus region, and to start engaging in international voluntary partnerships such as the Group on Earth Observations (GEO). CENS now envisions to scale up its SDI infrastructure to an environmental national SDI (nSDI) in order to support a wider range of geospatial services. This paper discusses several aspects and challenges of the envisioned strategy. First, we present how the current components of the implemented SDI benefit the scientific and environmental communities in Armenia. Second, we examine how the EGIDA methodology can be applied to support the process of scaling up the infrastructure to become a nSDI, one of the pilot studies in the EU/FP7 EOPOWER project. Finally, we discuss the potential of future full-scale provision of geospatial services in Armenia and how these could benefit the various stakeholders involved in Armenia and in the South Caucasus region

Leading the way toward an environmental National Spatial Data Infrastructure in Armenia

Once the most industrialized republic of the Soviet Union, Armenia inherited a dramatic ecological situation from the Soviet era. As the key national environmental academic entity, the Center for Ecological-Noosphere Studies (CENS) of the National Academy of Sciences of the Republic of Armenia has a strong national role in delivering authoritative environmental information and data sets. To enhance data sharing towards its stakeholders, CENS engaged in recent years in several international capacity building projects directed to the setting up of an environmental Spatial Data Infrastructure (SDI). These activities were successful in showing the potential of data sharing in Armenia, to gain visibility in the country and the South Caucasus region, and to start engaging in international voluntary partnerships such as the Group on Earth Observations (GEO). CENS now envisions to scale up its SDI infrastructure to an environmental national SDI (nSDI) in order to support a wider range of geospatial services. This paper discusses several aspects and challenges of the envisioned strategy. First, we present how the current components of the implemented SDI benefit the scientific and environmental communities in Armenia. Second, we examine how the EGIDA methodology can be applied to support the process of scaling up the infrastructure to become a nSDI, one of the pilot studies in the EU/FP7 EOPOWER project. Finally, we discuss the potential of future full-scale provision of geospatial services in Armenia and how these could benefit the various stakeholders involved in Armenia and in the South Caucasus region

An interoperable web portal for parallel geoprocessing of satellite image vegetation indices

The main objective of this paper is to introduce a portal of geoprocessing services that can be used to compute either a single vegetation index or a combination of vegetation indices, as a workflow. High Performance Computing (HPC) resources are used for the calculations, and the Web Processing Service (WPS) standard is used to handle the requests from and the responses to the portal. In case of a workflow, a single node of the cluster is dedicated to each index, and the number of used cores depends on the complexity of the task. In addition, based on a series of experiments made to accelerate remote sensing image processing, a parallelization method within the computational node is automatically chosen depending on the complexity of the operations and the amount of data. The suggested algorithm optimizes the processing by selecting the best methodology (serial or parallel) and the number of cores to efficiently manipulate and distribute the data. The interoperable web portal, Spatial Data Infrastructure (SDI) and the heterogeneous resources of HPC cluster are located in the same local area network, and the cluster nodes have access to the data via network file system sharing. The use of standardized web services makes it possible to use remote data as inputs